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UTD CS 7301 - Building Trustworthy Semantic Webs

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Building Trustworthy Semantic WebsOutlineOverview of MLS/DBMSSummary of DevelopmentsTaxonomy for MLS/DBMSsIntegrity LockOperating System Providing Mandatory Access ControlExtended KernelTrusted SubjectDistributed Approach - IDistributed Approach IISome Challenges: Inference ProblemSome Challenges: PolyinstantiationSome Challenges: Covert ChannelMultilevel Secure Data Model: Classifying DatabasesMultilevel Secure Data Model: Classifying RelationsMultilevel Secure Data Model: Classifying Attributes/ColumnsMultilevel Secure Data Model: Classifying Tuples/RowsMultilevel Secure Data Model: Classifying ElementsMultilevel Secure Data Model: Classifying ViewsMultilevel Secure Data Model: Classifying MetadataStatus and DirectionsMultilevel Semantic Web TechnologiesBuilding Trustworthy Semantic WebsDr. Bhavani ThuraisinghamThe University of Texas at DallasMultilevel Secure Data Management and its implications to Multilevel semantic web technologiesOctober 27, 2008OutlineWhat is an MLS/DBMS?Summary of DevelopmentsChallengesData Models Implications for semantic webOverview of MLS/DBMSWhat is an MLS/DBMSUsers are cleared at different security levelsData in the database is assigned different sensitivity levels--multilevel databaseUsers share the multilevel databaseMLS/DBMS is the software that ensures that users only obtain information at or below their levelIn general, a user reads at or below his level and writes at his levelNeed for an MLS/DBMSOperating systems control access to files; coarser grain of granularityDatabase stores relationships between dataContent, Context, and Dynamic access controlTraditional operating systems access control to files is not sufficientNeed multilevel access control for DBMSsSummary of DevelopmentsEarly Efforts 1975 – 1982; example: Hinke-Shafer approach Air Force Summer Study, 1982Research Prototypes (Integrity Lock, SeaView, LDV, etc.); 1984 - PresentTrusted Database Interpretation; published 1991Commercial Products; 1988 - PresentTaxonomy for MLS/DBMSsIntegrity Lock Architecture: Trusted Filter; Untrusted Back-end, Untrusted Front-end. Checksum is computed by the filter based on data content and security level. Checksum recomputed when data is retrieved. Operating Systems Providing Access Control/ Single Kernel: Multilevel data is partitioned into single level files. Operating system controls access to the filedExtended Kernel: Kernel extensions for functions such as inference and aggregation and constraint processingTrusted Subject: DBMS provides access control to its own data such as relations, tuples and attributesDistributed: Data is partitioned according to security levels; In the partitioned approach, data is not replicated and there is one DBMS per level. In the replicated approach lower level data is replicated at the higher level databasesIntegrity LockDatabaseTrusted Agentto computechecksumsSensorData ManagerUntrustedData ManagerCompute ChecksumBased on stream data valueand Security level;Store data value, Security level and ChecksumCompute ChecksumBased on data valueand Security level retrievedfrom the stored databaseOperating System Providing Mandatory Access ControlUnclassifieddeviceSecretdeviceTopSecretdeviceMultilevelData ManagerUnclassifiedDataSecretDataTopSecretDataExtended KernelMultilevelDataKernel ExtensionsTo enforce additional security policies enforced on datae.g., security constraints, privacy constraints, etc.MultilevelData ManagerTrusted SubjectUnclassifieddeviceSecretdeviceTopSecretdeviceMultilevelData ManagerMultilevelDataTrustedComponentDistributed Approach - IUnclassifiedData ManagerTopSecretData ManagerUnclassifiedDataSecretDataTopSecretDataTrusted Agentto manage Aggregated DataSecretData Manager UnclassifiedData ManagerTopSecretData ManagerUnclassifiedDataSecretDataTopSecretDataTrusted Agentto manage Aggregated DataSecretData ManagerDistributed Approach IIUnclassifiedData ManagerTopSecretData ManagerUnclassifiedDataSecret + UnclassifiedDataTopSecretSecret + UnclassifiedDataTrusted Agentto manage Aggregated DataSecretData ManagerSome Challenges: Inference ProblemInference is the process of forming conclusions from premisesIf the conclusions are unauthorized, it becomes a problemInference problem in a multilevel environmentAggregation problem is a special case of the inference problem - collections of data elements is Secret but the individual elements are UnclassifiedAssociation problem: attributes A and B taken together is Secret - individually they are UnclassifiedSome Challenges: PolyinstantiationMechanism to avoid certain signaling channelsAlso supports cover storiesExample: John and James have different salaries at different levelsEMPSS# Name Salary1 John 20 2 Paul 303 James 401 John 70 4 Mary 803 James 60LevelUUUSSSSome Challenges: Covert ChannelDatabase transactions manipulate data locks and covertly pass informationTwo transactions T1 and T2; T1 operates at Secret level and T2 operates at Unclassified levelRelation R is classified at Unclassified levelT1 obtains read lock on R and T2 obtains write lock on R T1 and T2 can manipulate when they request locks and signal one bit information for each attempt and over time T1 could covertly send sensitive information to T1Multilevel Secure Data Model: Classifying DatabasesEMPSS# Ename Salary D# 1 John 20K 102 Paul 30K 203 Mary 40K 20DEPTD# Dname Mgr10MathSmith20 Physics JonesDATABASE D: Level = SecretMultilevel Secure Data Model: Classifying RelationsEMP: Level = SecretSS# Ename Salary D# 1 John 20K 102 Paul 30K 203 Mary 40K 20DEPT: Level = UnclassifiedD# Dname Mgr10MathSmith20 Physics JonesMultilevel Secure Data Model: Classifying Attributes/ColumnsEMPSS#: S Ename: U Salary: S D#: U 1 John 20K 102 Paul 30K 203 Mary 40K 20DEPTD#: UDname: U Mgr: S10MathSmith20 Physics JonesU = UnclassifiedS = SecretMultilevel Secure Data Model: Classifying Tuples/RowsEMPSS# Ename Salary D# 1 John 20K 10 U2 Paul 30K 20 S3 Mary 40K 20 TSDEPTD# Dname Mgr10MathSmith U20 Physics Jones CLevel LevelU = UnclassifiedC = ConfidentialS = SecretTS = TopSecretMultilevel Secure Data Model: Classifying ElementsEMPSS#: Ename: Salary D#:1, S John, U 20K, C 10, U2, S Paul, U 30K, S 20, U3, S Mary, U 40K, S 20, UDEPTD#: UDname:


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